We simulate 10 trials for each \(\epsilon \) , while for each trial the updates of 60 consecutive spikes are accumulated, as suggested in [1]}, to finally compute the new synaptic weight \(\hat{w}_{ij}\) .
When there are no longer weight updates for every spike, the update will be terminated.
Here, we set the learning rate for the transition and holding updates to \(\eta _{\mathcal {T}} = 0.05\)  and \(\eta _{\mathcal {H}} = 0.001,\)  respectively. Other parameters are \(w_{ij} = 1, b_i = b_j = 0\)  .
